Since errors and frauds appear differently in accounting data and hence call for various analytical interpretations, data-driven fraud detection starts with a clear conceptual separation between the two (Albrecht et al., 2006) . Forensic accountants, auditors, and analysts that use data analytics to find anomalies in massive amounts of financial and non-financial data must comprehend this distinction.

Unintentional misstatements or inconsistencies in financial reports and accounting records are referred to as errors (Achmad et al., 2022). These usually result from human error, including inaccurate data entry, incorrect account classification, misinterpretation or improper use of accounting standards, or information system limits and malfunctions. Errors are the result of flaws in procedures, controls, training, or system architecture rather than intentional attempts to deceive. Errors in data analysis frequently manifest as discrete, erratic, and random anomalies. Unusual values may be produced, for instance, by a transposed number, an improper posting period, or a one-time computation error; however, these anomalies typically lack strategic organization and repetition.

Frauds, on the other hand, are deliberate actions intended to mislead users of financial data for individual or corporate benefit (Bigelow, 1887; Bello and Olufemi, 2024). Fraud entails intentional manipulation, cover-up, or misrepresentation of transactions, balances, or disclosures as well as deliberate decision-making. Falsifying revenues, falsifying vendors, manipulating expenses, or intentionally bypassing internal controls are typical examples. Because fraud is deliberate, those who do it frequently repeat identical acts over time in order to maximize rewards or hide past wrongdoing. Therefore, instead of random noise, fraud leaves behind regular patterns in data.

Both mistakes and frauds produce anomalies, or transactions or data points that diverge from expected norms, according to a data-driven viewpoint (Krotoski, 2012). These abnormalities vary greatly in nature, though. While fraud-related abnormalities frequently exhibit consistency, regularity, and logical intent, errors are typically random, unintentional, and non-recurring. For example, recurrent transactions with the same vendor at odd times, consistent round-number quantities, or repeated transactions just below approval thresholds may indicate fraudulent activity rather than a straightforward mistake.

In analytical fraud detection, this distinction is essential (de Oliveira et al., 2021). Data analytics finds symptoms and warning signs hidden in big databases, but it doesn’t “prove” fraud. Because fraud is structured and repeated, it is especially well-suited for detection using analytical methods including digital analysis, trend analysis, pattern recognition, and outlier identification. Even while errors are evident, they usually call for process evaluations and control enhancements rather than investigative measures.

In conclusion, analysts can more effectively evaluate anomalies, prioritize investigative efforts, and use appropriate professional judgment when they can differentiate between errors and frauds. While acknowledging that not all abnormalities point to fraud and that rigorous analysis is still necessary, data-driven fraud detection makes use of the expected patterns that deliberate wrongdoing creates.

Achmad T, Ghozali I and Pamungkas ID. (2022) Hexagon fraud: Detection of fraudulent financial reporting in state-owned enterprises Indonesia. Economies 10: 13.
Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
Bello OA and Olufemi K. (2024) Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer science & IT research journal 5: 1505-1520.
Bigelow MM. (1887) Definition of fraud. LQ Rev. 3: 419.
de Oliveira EF, Pedrosa CKA, Silva SLP, et al. (2021) THE USE OF FRAUD DETECTION TECHNOLOGIES IN THE COVID-19 PANDEMIC. Revista Contabilidade E Controladoria-Rc C 13: 156-177.
Krotoski AK. (2012) Data-driven research: Open data opportunities for growing knowledge, and ethical issues that arise. Insights: the UKSG Journal 25: 28-32.